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bioRxiv · 10.1101/2024.08.29.610242

Inferring super-resolved spatial metabolomics from microscopy

Rappez, L.·Haase, K.
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Abstract

Current spatial metabolomics techniques have transformed our understanding of cellular metabolism, yet accessible methods are limited in spatial resolution due to sensitivity constraints. MetaLens, a deep generative approach, disrupts this trade-off by quantitatively propagating cellular-resolution in situ imaging mass spectrometry readouts to subcellular scales through integration with high-resolution light microscopy. MetaLens identifies subcellular metabolic domains with distinct molecular composition, enabling accessible label-free subcellular metabolomic analysis from microscopy.

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Rappez, L., Haase, K.. 2024-08-29. Inferring super-resolved spatial metabolomics from microscopy. https://doi.org/10.1101/2024.08.29.610242

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